Role: GenAI Lead Engineer

Experience: 8+ years total (with significant hands-on GenAI / LLM work)

Job Overview:

We are seeking an innovative and highly skilled Lead Generative AI (GenAI) Engineer to spearhead the design, development, and deployment of advanced AI-powered solutions. In this role, you will lead a team of engineers and data scientists to harness cutting-edge Generative AI technologies and implement them to solve complex business problems, enhance user experiences, and drive innovation. This role combines deep technical expertise, leadership, and a strong understanding of AI trends and tools.

Key Responsibilities:

• Technical Leadership:
• Lead the end-to-end design and implementation of Generative AI solutions.
• Provide technical guidance and mentorship to engineers and data scientists working on GenAI projects.
• Stay updated with the latest trends, research, and advancements in Generative AI and Large Language Models (LLMs).
• Solution Development:
• Architect, train, and fine-tune state-of-the-art LLMs and generative AI models
• Develop and optimize pipelines for prompt engineering, retrieval-augmented generation (RAG), and domain-specific fine-tuning.
• Develop and deploy generative AI models, particularly focusing on ChatGPT, using Python on Azure or AWS Platform or .Net on Azure platform
• Ensure scalability, performance, and security of AI solutions deployed in production.
• Integration and Deployment:
• API Development: Ability to define and deliver API access for GenAI services, facilitating integration with other systems and applications.
• Collaborate with software engineering teams to integrate GenAI solutions into enterprise applications and services.
• Utilize cloud platforms (e.g., Azure, AWS, or GCP) to deploy and manage AI models and APIs.
• Leverage MLOps practices for continuous model monitoring, retraining, and improvement.
• Data Strategy and Preparation:
• Collaborate with data engineering teams to ensure high-quality data acquisition, preprocessing, and augmentation for model training and fine-tuning.
• Implement data governance and privacy practices in line with organizational policies.
• Innovation and Research:
• Experiment with new generative AI techniques, such as multimodal AI, reinforcement learning with human feedback (RLHF), and active learning.
• Evaluate and recommend AI frameworks, libraries, and platforms for project requirements.
• Stakeholder Collaboration:
• Work closely with product managers, business stakeholders, and UX designers to define AI-powered product features and use cases.
• Present technical concepts, project progress, and AI capabilities to non-technical audiences.

Key Requirements

• Technical Skills:
• Hands-on experience with cloud platforms and services for AI/ML, such as Azure AI Services, Azure Machine Learning, AWS Bedrock, or Google Vertex AI.
• Hands on experience in any of LLMs such as OpenAI’s ChatGPT Models , Gemini, Llama 2 ,Claude 2 ,Grok
• Hands on experience in any of the agentic frameworks like LangChain, Semantic kernel, AutoGen, CrewAi
• Hands on experience using any of vector database like Chroma, Pinecone, Weaviate, Faiss
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF)
• Strong expertise in LLMs and generative AI frameworks like OpenAI, Hugging Face Transformers, or similar platforms.
• Deep understanding of natural language processing (NLP) concepts, including tokenization, embeddings, and sequence-to-sequence models.
• Proficiency in Python and libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Experience in CI/CD pipeline management and automation tools, particularly within the Azure DevOps environment. Knowledge of containerization (e.g., Docker) and orchestration tools is also important
• Familiarity with MLOps tools and practices, such as MLflow, Kubeflow, or Docker.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (Ph.D. preferred).
• 8+ years of experience in AI/ML engineering, with 2+ years specifically in Generative AI.
• Minimum of 2 years of experience in building Conversational AI applications using cloud-based services and in orchestrating AI/ML services for building a complete solution
• Minimum of 6 years of extensive full-time experience in Data Analysis, Statistics, Machine Learning, or Computer Science
• Proven track record of leading AI projects from inception to production.
• Experience with multimodal AI and advanced techniques like Tree-of-Thoughts, Retrieval-Augmented Generation (RAG), or Reinforcement Learning with Human Feedback (RLHF).
• Certifications in AI/ML or cloud platforms (e.g., Azure AI Engineer, AWS Certified Machine Learning).

Job Overview:

We are seeking a seasoned AI Developer to lead the design and implementation of next-generation Agentic AI solutions. You won't just be building chatbots; you will be architecting autonomous systems where multiple specialized AI agents collaborate to solve complex, long-horizon business problems. You will leverage a multi-cloud strategy to deploy scalable, resilient, and "reasoning-heavy" AI workflows.

 Key Responsibilities

• Agentic Architecture: Design and develop autonomous agents capable of independent planning, tool-use (function calling), context & prompt engineering, and self-correction.
• Multi-Agent Orchestration: Implement LangGraph to manage task delegation, conflict resolution, and collaborative reasoning between specialized agents.
• Advanced RAG & Memory: Build sophisticated Retrieval-Augmented Generation (RAG) pipelines with episodic and semantic memory to ensure agents maintain context over long interactions.
• Cloud-Native Deployment: Architect and deploy AI services across AWS (Bedrock/SageMaker), Azure (OpenAI Service/Foundry), ensuring high availability and cost optimization.
• Guardrails & Ethics: Implement rigorous evaluation frameworks (e.g., Ragas, TruLens) and safety guardrails to prevent hallucinations and ensure responsible agent behavior.
• Performance Tuning: Optimize LLM latency and throughput using techniques like prompt caching, quantization, and specialized model routing.
• Prompt engineering & Context engineering.

Key Skills:

• Core AI: 4+ years of professional experience in AI/ML, with at least 1.5+ years focused specifically on Generative AI and LLM orchestration.
• Agent Frameworks: Hands-on expertise with LangGraph. Proven ability to build "loops" and "state machines" rather than just linear chains.
• Programming: Mastery of Python (Asyncio, Pydantic, FastAPI). Experience with TypeScript/Node.js is a significant plus.
• Cloud Infrastructure:
• AWS: Bedrock, Lambda, Step Functions, S3.
• Azure: Azure OpenAI, AI Search, CosmosDB, PostgreSQL.
• Data & Memory: Proficiency with Vector Databases like Pinecone, Weaviate, Milvus, or pgvector. Understanding of graph databases (Neo4j) for knowledge-graph-enhanced RAG.
• DevOps/AIOps: Experience with Docker, Kubernetes, and CI/CD pipelines specifically for AI (LLMOps).

 Preferred Qualifications

• Contributions to open-source AI frameworks or research publications in MAS.
• Experience with Model Context Protocol (MCP) or Agent-to-Agent (A2A) communication standards.
• Familiarity with fine-tuning techniques (LoRA, QLoRA) for specialized agent tasks.

Job Overview:

We are seeking an experienced Senior Data Engineer to build and optimize scalable data platforms using Microsoft Fabric, Databricks (and/or Snowflake). The role focuses on designing reliable data pipelines, lakehouse and warehouse models, and semantic layers that enable enterprise analytics, BI, and AI/Gen AI use cases.

You will work closely with analytics, BI, and data science teams to deliver high-quality, performant, and governed data solutions, while driving best practices in data engineering, optimization, and platform design.

Key Responsibilities

• Design, build, and maintain end-to-end data solutions on Microsoft Fabric, Databricks, including Pipelines, Notebooks, Lakehouse, Data Warehouse, and Semantic Models.
• Implement scalable data ingestion, transformation, and loading (ETL/ELT) using Fabric Pipelines and PySpark.
• Develop robust data models and schemas optimized for analytics, reporting, and AI-driven consumption.
• Create and maintain semantic models to support Power BI and enterprise BI solutions.
• Engineer high-performance data solutions that meet requirements for throughput, scalability, quality, and security.
• Author efficient PySpark and SQL code for large-scale data transformation, data quality management, and business rule processing.
• Build reusable framework components for metadata-driven pipelines and automation.
• Optimize Lakehouse and Data Warehouse performance, including partitioning, indexing, Delta optimization, and compute tuning.
• Develop and maintain stored procedures and advanced SQL logic for operational workloads.
• Design and prepare feature-ready datasets for AI and GenAI applications.
• Collaborate with data scientists and ML engineers to productionize AI pipelines.
• Implement data governance and metadata practices required for responsible AI.
• Leverage Fabric, Databricks capabilities to orchestrate and monitor AI-related data workflows.
• Apply data governance, privacy, and security standards across all engineered assets.
• Implement monitoring, alerting, and observability best practices for pipelines and compute workloads.
• Drive data quality initiatives, including validation frameworks, profiling, and anomaly detection.
• Partner with analytics, BI, data science, and product teams to understand requirements and translate them into technical solutions.
• Mentor junior engineers and contribute to engineering standards and patterns.
• Participate in architecture reviews, technical design sessions, and roadmap planning.
• Develop and deliver dashboards and reports using Power BI and Tableau.

Required Skills:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent experience.
• 7+ years of professional experience in data engineering or a related discipline.
• Mandatory hands-on expertise with Microsoft Fabric, Databricks or Snowflake including:
  • Pipelines
  • Notebooks
  • Lakehouse
  •  Data Warehouse
  • Semantic Models
• Advanced proficiency in PySpark for distributed data processing.
• Strong command of SQL for analytical and operational workloads.
• Experience developing and optimizing stored procedures and complex SQL transformations.
• Understanding of GenAI architectures, vectorization, embedding pipelines, and data preparation for LLM use cases.
• Strong knowledge of data modeling, ETL/ELT patterns, and modern data lakehouse principles.
• Experience designing and optimizing large-scale data pipelines in cloud environments (Azure preferred).
• Excellent problem-solving skills with the ability to analyze complex data workflows.
• Proficiency in creating interactive dashboards and reports using Power BI and Tableau.

Job Overview:

We are looking for a Senior GenAI Developer to design, build, and productionize agentic AI systems—LLM-powered agents that can plan, use tools, orchestrate workflows, and operate reliably under enterprise constraints. You will own key parts of the agent architecture (planning, tool use, memory, evaluation, safety/guardrails, and observability) and deliver end-to-end solutions across RAG, function/tool calling, multi-agent coordination, and scalable deployment.

Key Responsibilities

• Design and implement agentic systems: single-agent and multi-agent architectures (planner/executor, supervisor-worker, routing, reflection, critique, task decomposition).
• Build robust tool-using agents: function calling, tool schemas, tool authorization, retries, rate limiting, and sandboxing.
• Implement RAG + memory patterns: retrieval strategies, hybrid search, context assembly, long-term memory, and grounding/citation behaviors.
• Develop workflow orchestration for agent execution (state machines/graphs), concurrency controls, and deterministic execution where possible.
• Productionize GenAI services: APIs, background jobs, streaming responses, caching, and cost/latency optimization.
• Establish agent evaluation: golden sets, simulation-based evals, LLM-as-judge with mitigations, task success metrics, regression testing.
• Build observability and safety: tracing, token/tool telemetry, anomaly detection, prompt injection defenses, data leakage prevention, policy enforcement.
• Collaborate with product, security, and platform teams to deliver enterprise-ready solutions and integrate with internal systems (data, identity, workflow).
• Mentor engineers, set coding standards, and contribute to architecture reviews and technical roadmaps.

Required Qualifications

• 6+ years software engineering experience; 2+ years building LLM/GenAI systems in production.
• Strong programming skills in Python (required) and/or TypeScript/Node.js.
• Hands-on experience building agents (tool calling, planning, routing, multi-step workflows) beyond simple chatbots.
• Solid understanding of prompting, context window management, grounding, hallucination failure modes, and mitigation strategies.
• Experience with RAG: embeddings, vector databases, chunking strategies, hybrid retrieval, re-ranking.
• Proven ability to ship production services: Docker/Kubernetes, REST/gRPC, CI/CD, monitoring, incident response basics.
• Strong data/security instincts: secrets management, PII handling, secure tool access, least privilege

Job Overview:

We are seeking an experienced Azure Cloud Application Architect to design and implement enterprise-grade, cloud-native solutions using Azure services. This role involves defining best practices for architecture, governance, security, and cost optimization while enabling CI/CD and Infrastructure as Code. You will provide technical leadership, mentor teams, and act as a trusted advisor to clients, driving modernization, migration, and innovation initiatives. The position also includes pre-sales and post-sales engagement, building strategic partnerships, and contributing to thought leadership through white papers and presentations.

Experience: 10+ years

Key Responsibilities:

• Design and implement cloud-native application architectures using Azure services (App Services, AKS, Functions, Service Bus, etc.).
• Collaborate with stakeholders to translate business requirements into technical solutions.
• Establish governance, cost optimization, and security best practices for Azure applications.
• Work closely with DevOps teams to enable CI/CD pipelines and infrastructure as code (IaC).
• Provide technical leadership, mentoring, and guidance to development teams.      
• Define and implement best practices architecture design, deployment, and operations.
• Design highly available, scalable, and cost-effective cloud solutions meeting business requirements.
• Collaborate with cross-functional teams, including DevOps, development, and business units, to align cloud solutions with organizational goals.
• Analyse complex technical issues, provide recommendations, and lead the resolution efforts for cloud-related challenges.
• Build capabilities, offerings, service accelerators and differentiators.
• Build a go-to-market strategy that highlights our cloud offerings that best support our customers' challenges.
• Innovation and Thought leadership, bringing out white papers, blogs, presentations, and collaterals.
• Responsible for delivering solutions architecture for major clients.
• Client Relationship Management (both pre-sales, Solution & post-sales)
• Be client CIO/CTO's trusted advisor.
• Deliver superior value and conduct project reviews.
• Enable teams to deliver better value in projects.
• Coaching, Mentoring and Provide team a clear direction.

Should be hands on:

• Create and maintain architectural documentation, reports, and presentations for stakeholders.
• Define solution blueprints and oversee application modernization and migration to Azure.
• Pre-sales / post-sales (play an active role in winning business)
• Build and grow relationships with cloud and alliance partners to expand our relationship, offerings, and revenues.
• Build relationships with key client decision makers and influences in partnership with sales and marketing.

Key Skills:

• Expertise in designing high-level, enterprise grade solution architecture
• At least 10+ years of relevant experience as an Azure Cloud App Architect
• Ability to manage estimates, tasks, escalations, risks/issues
• Ability to clearly articulate concepts to people with varying technical background
• Azure Certified Solution Architect - Az 303/304/305.

Job Overview:

We are looking for a senior technical lead to design, scale, and govern enterprise-grade data platforms across Microsoft Fabric, Databricks, and Snowflake. The role focuses on defining data architecture standards, enabling AI/GenAI-ready platforms, leading engineering teams, and delivering secure, high-performance cloud data solutions that support analytics and business outcomes.

Key Responsibilities

• Lead the design and implementation of scalable data platforms using Microsoft Fabric, Databricks and Snowflake including Pipelines, Notebooks, Lakehouse, Data Warehouse, and Semantic Models.
• Define end-to-end data architecture standards, patterns, and best practices across ingestion, storage, transformation, modeling, and consumption layers.
• Guide technical decisions on compute patterns, storage optimization, pipeline orchestration, and data modeling frameworks.
• Evaluate and integrate emerging technologies to modernize the platform and enable AI/GenAI workloads.
• Partner with AI/ML teams to productionize models and integrate them with enterprise data workflows.
• Ensure data governance, lineage tracking, and metadata standards to support trustworthy AI initiatives.
• Champion data governance, security, privacy, and compliance requirements within the platform.
• Implement observability frameworks for pipeline monitoring, performance tuning, and reliability engineering.
• Drive continuous improvement initiatives to enhance platform robustness and engineering productivity.
• Work closely with product owners, business stakeholders, architects, and analytics teams to translate requirements into scalable technical solutions.
• Serve as the primary technical point of contact for data platform projects and stakeholders.
• Communicate technical roadmaps, project risks, and delivery plans clearly and effectively.
• Develop and deliver dashboards and reports using Power BI and Tableau.

Key Skills:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent professional experience.
• 12+ years of experience in data engineering, data platform development, or cloud data architecture.
• 3+ years in a technical leadership or team management role.
• Mandatory deep hands-on expertise with Microsoft Fabric, Snowflake or Databricks:
  • Pipelines
  • Notebooks
  • Lakehouse
  • Data Warehouse
  • Semantic Models
• Strong proficiency in PySpark, distributed compute, and data transformation frameworks.
• Advanced SQL capabilities and experience building stored procedures for transactional and analytical workloads.
• Proven track record in designing and delivering large-scale data platforms in cloud environments (Azure preferred).
• Solid understanding of AI/ML data workflows, GenAI dataset preparation, and integration patterns.
• Demonstrated ability to lead engineering teams, drive architectural decisions, and oversee complex technology implementations.
• Strong communication, stakeholder management, and technical documentation skills.
• Proficiency in creating interactive dashboards and reports using Power BI and Tableau.